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Flexible Joint Hierarchical Gaussian Process Model for Longitudinal and Recurrent Event Data

Su, Weiji
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1595850414934069

Abstract Details

2020, PhD, University of Cincinnati, Arts and Sciences: Mathematical Sciences.
Jointly modeling two types of longitudinal markers makes optimal use of the available information and serves to investigate the joint evolution of the two processes, to examine the underlying association and to evaluate the surrogate markers simultaneously. In this dissertation, we develop a series of joint models for the longitudinal repeated measurements, including continuous, repeated binary, and time to recurrent event data. Our goal is to extend the joint model with more flexibility via parametric, semi-parametric and nonparametric methods to capture various features in the data. A hierarchical Gaussian process is incorporated into the proposed joint model framework to explain the characteristics of both population and personalized variation and provide dynamic predictions. In analyzing the longitudinal continuous and repeated binary data, we incorporate a family of parametric link functions into the proposed joint model to obtain flexibility in handling skewness in the probability response curves. In jointly modeling the longitudinal and recurrent time-to-event data, we utilize both semi-parametric and non-parametric methods to monitor the non-linearity in population evolution and heterogeneity. Furthermore, we exhibit the application of the proposed joint model in examining the impact of various risk factors. We employ Bayesian approaches in model construction and estimation. The proposed models are compared with existing joint modeling approaches. Particularly, we incorporate the idea of likelihood decomposition and develop the model comparison criterion to facilitate performance assessment of each submodel separately. We carry out extensive simulation studies in each of the models we proposed. The purpose of the simulation studies is to show the properties, implementation, performance as well as potential problems of the proposed models compared with existing methods. Joint modeling is of particular importance in clinical studies. Our real data application focuses on a data set from pediatric patients at the Cystic Fibrosis (CF) Center at Cincinnati Children's Hospital Medical Center (CCHMC). The results from both simulation studies and the real data applications show the practical importance of our proposed methods.
Xia Wang, Ph.D. (Committee Chair)
Xuan Cao, Ph.D. (Committee Member)
Won Chang, Ph.D. (Committee Member)
Siva Sivaganesan, Ph.D. (Committee Member)
Rhonda Szczesniak, Ph.D. (Committee Member)
112 p.
Statistics
Bayesian model; joint model; flexible link function; Gaussian process; recurrent event; spline

Recommended Citations

Citations

  • Su, W. (2020). Flexible Joint Hierarchical Gaussian Process Model for Longitudinal and Recurrent Event Data [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1595850414934069

    APA Style (7th edition)

  • Su, Weiji. Flexible Joint Hierarchical Gaussian Process Model for Longitudinal and Recurrent Event Data. 2020. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1595850414934069.

    MLA Style (8th edition)

  • Su, Weiji. "Flexible Joint Hierarchical Gaussian Process Model for Longitudinal and Recurrent Event Data." Doctoral dissertation, University of Cincinnati, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1595850414934069

    Chicago Manual of Style (17th edition)

ucin1595850414934069
41
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This open access ETD is published by University of Cincinnati and OhioLINK.